Systems and methods for estimating flight range of an electric aircraft

ABSTRACT

A system for estimating flight range of an electric aircraft. The system generally includes at least a sensor and a flight controller. The at least a sensor is communicatively connected to at least a flight component. The at least a sensor is configured to detect a performance datum of the at least a flight component. The flight controller is communicatively connected to the at least a sensor. The flight controller is configured to receive the performance datum from the at least a sensor, determine an energy performance datum from the performance datum, determine a flight performance datum from the performance datum, generate a projected flight range datum as a function of the energy performance datum and the flight performance datum, and display the projected flight range datum. A method for estimating flight range of an electric aircraft is also provided.

FIELD OF THE INVENTION

The present invention generally relates to the field of aircraft flight.In particular, the present invention is directed to systems and methodsfor estimating flight range of an electric aircraft.

BACKGROUND

It is important to have information on an aircraft's range of flight.This information can become even more valuable when the aircraft is inflight. However, it can be a challenging task to obtain reliable andtimely flight range information for many types of aircrafts.

SUMMARY OF THE DISCLOSURE

In an aspect a system for estimating flight range of an electricaircraft is provided. The system generally includes at least a sensorand a flight controller. The at least a sensor is communicativelyconnected to at least a flight component. The at least a sensor isconfigured to detect a performance datum of the at least a flightcomponent. The flight controller is communicatively connected to the atleast a sensor. The flight controller is configured to receive theperformance datum from the at least a sensor, determine an energyperformance datum from the performance datum, determine a flightperformance datum from the performance datum, generate a projectedflight range datum as a function of the energy performance datum and theflight performance datum, and display the projected flight range datum.

In another aspect a method for estimating flight range of an electricaircraft is provided. The method generally includes, detecting, by atleast a sensor communicatively connected to at least a flight component,a performance datum of the at least a flight component, receiving, by aflight controller communicatively connected to the at least a sensor,the performance datum from the at least a sensor, determining, by theflight controller, an energy performance datum from the performancedatum, determining, by the flight controller, a flight performance datumfrom the performance datum, generating, by the flight controller, aprojected flight range datum as a function of the energy performancedatum and the flight performance datum, and displaying, by the flightcontroller, the projected flight range datum.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagrammatic representation of an exemplary embodiment of anelectric aircraft;

FIG. 2 is a block diagram of an exemplary embodiment of a system forestimating flight range of an electric aircraft;

FIG. 3 is a block diagram of an exemplary embodiment of a flightcontroller;

FIG. 4 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 5 is a block diagram of an exemplary embodiment of a method forestimating flight range of an electric aircraft; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, “upward”, “downward”, “forward”,“backward” and derivatives thereof shall relate to the invention asoriented in FIG. 1 . Furthermore, there is no intention to be bound byany expressed or implied theory presented in the preceding technicalfield, background, brief summary or the following detailed description.It is also to be understood that the specific devices and processesillustrated in the attached drawings, and described in the followingspecification, are simply exemplary embodiments of the inventiveconcepts defined in the appended claims. Hence, specific dimensions andother physical characteristics relating to the embodiments disclosedherein are not to be considered as limiting, unless the claims expresslystate otherwise.

At a high level, aspects of the present disclosure are directed tosystems and methods for estimating flight range of an electric aircraft.In an embodiment, this is accomplished by at least a sensor and a flightcontroller of system. Aspects of the present disclosure can be used toestimate aircraft's flight range by detecting a performance datum ofaircraft and advantageously utilizing flight controller to generate aprojected flight range datum of aircraft. Aspects of the presentdisclosure can also be used to display projected flight range datum to asuitable user, such as, for example and without limitation, a pilotonboard or remote from aircraft. Aspects of the present disclosure,advantageously, allow for substantially continuous determination of anenergy performance datum and a flight performance datum to generateprojected flight range datum in real-time. Projected flight range datummay be progressively updated as the flight progresses.

Providing accurate and timely flight range estimates can be challenging.Aspects of the present disclosure desirably achieve improved accuracyand efficiency in generating flight range estimates by detecting of aplurality of data associated with performance of electric aircraft andits flight, and calculating flight range estimates and updating them inreal-time. For instance, energy source performance, environmental andspeed data may efficaciously be utilized by flight controller toaccurately map out a projected flight range for aircraft. This may bedisplayed to multiple locations using any suitable metric such as timeand/or distance. Exemplary embodiments illustrating aspects of thepresent disclosure are described below in the context of severalspecific examples.

Referring now to FIG. 1 , an exemplary embodiment of an electricaircraft 100 which includes, and/or operates in conjunction with, asystem for estimating flight range thereof is illustrated. In anembodiment, electric aircraft 100 may be an electric vertical takeoffand landing (eVTOL) aircraft. As used in this disclosure, an “aircraft”is any vehicle that may fly by gaining support from the air. As anon-limiting example, aircraft may include airplanes, helicopters,commercial, personal and/or recreational aircrafts, instrument flightaircrafts, drones, electric aircrafts, airliners, rotorcrafts, verticaltakeoff and landing aircrafts, jets, airships, blimps, gliders,paramotors, quad-copters, unmanned aerial vehicles (UAVs) and the like.As used in this disclosure, an “electric aircraft is an electricallypowered aircraft such as one powered by one or more electric motors orthe like. In some embodiments, electrically powered (or electric)aircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. Electric aircraft may include one or more mannedand/or unmanned aircrafts. Electric aircraft may include one or moreall-electric short takeoff and landing (eSTOL) aircrafts. For example,and without limitation, eSTOL aircrafts may accelerate the plane to aflight speed on takeoff and decelerate the plane after landing. In anembodiment, and without limitation, electric aircraft may be configuredwith an electric propulsion assembly. Including one or more propulsionand/or flight components. Electric propulsion assembly may include anyelectric propulsion assembly (or system) as described in U.S.Nonprovisional application Ser. No. 16/703,225, filed on Dec. 4, 2019,and entitled “AN INTEGRATED ELECTRIC PROPULSION ASSEMBLY,” the entiretyof which is incorporated herein by reference.

Still referring to FIG. 1 , as used in this disclosure, a “verticaltake-off and landing (VTOL) aircraft” is one that can hover, take off,and land vertically. An “eVTOL aircraft”, as used in this disclosure, isan electrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft, eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff, airplanestyle landing, and/or any combination thereof. Rotor-based flight, asdescribed herein, is where the aircraft generates lift and propulsion byway of one or more powered rotors or blades coupled with an engine, suchas a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.“Fixed-wing flight”, as described herein, is where the aircraft iscapable of flight using wings and/or foils that generate lift caused bythe aircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

Still referring to FIG. 1 , electric aircraft 100, in some embodiments,generally includes a fuselage 104, a flight component 108 (or aplurality of flight components 108), a pilot control 120 and a flightcontroller 124. In one embodiment, flight components 108 include atleast a lift component 112 (or a plurality of lift components 112) andat least a pusher component 116 (or a plurality of pusher components116). In some embodiments, and as described further below with referenceto at least FIG. 2 , electric aircraft 100 includes, and/or operates inconjunction with, a system for estimating flight range thereof includingat least a sensor and a flight controller.

Still referring to FIG. 1 , as used in this disclosure a “fuselage” isthe main body of an aircraft, or in other words, the entirety of theaircraft except for the cockpit, nose, wings, empennage, nacelles, anyand all control surfaces, and generally contains an aircraft's payload.Fuselage 104 may include structural elements that physically support ashape and structure of an aircraft. Structural elements may take aplurality of forms, alone or in combination with other types. Structuralelements may vary depending on a construction type of aircraft such aswithout limitation a fuselage 104. Fuselage 104 may comprise a trussstructure. A truss structure may be used with a lightweight aircraft andcomprises welded steel tube trusses. A “truss,” as used in thisdisclosure, is an assembly of beams that create a rigid structure, oftenin combinations of triangles to create three-dimensional shapes. A trussstructure may alternatively comprise wood construction in place of steeltubes, or a combination thereof. In embodiments, structural elements maycomprise steel tubes and/or wood beams. In an embodiment, and withoutlimitation, structural elements may include an aircraft skin. Aircraftskin may be layered over the body shape constructed by trusses. Aircraftskin may comprise a plurality of materials such as plywood sheets,aluminum, fiberglass, and/or carbon fiber.

Still referring to FIG. 1 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of a system and method for estimating flightrange of an electric aircraft. In embodiments, fuselage 104 may beconfigurable based on the needs of the aircraft per specific mission orobjective. The general arrangement of components, structural elements,and hardware associated with storing and/or moving a payload may beadded or removed from fuselage 104 as needed, whether it is stowedmanually, automatedly, or removed by personnel altogether. Fuselage 104may be configurable for a plurality of storage options. Bulkheads anddividers may be installed and uninstalled as needed, as well aslongitudinal dividers where necessary. Bulkheads and dividers may beinstalled using integrated slots and hooks, tabs, boss and channel, orhardware like bolts, nuts, screws, nails, clips, pins, and/or dowels, toname a few. Fuselage 104 may also be configurable to accept certainspecific cargo containers, or a receptable that can, in turn, acceptcertain cargo containers.

Still referring to FIG. 1 , electric aircraft 100 may include aplurality of laterally extending elements attached to fuselage 104. Asused in this disclosure a “laterally extending element” is an elementthat projects essentially horizontally from fuselage, including anoutrigger, a spar, and/or a fixed wing that extends from fuselage. Wingsmay be structures which include airfoils configured to create a pressuredifferential resulting in lift. Wings may generally dispose on the leftand right sides of the aircraft symmetrically, at a point between noseand empennage. Wings may comprise a plurality of geometries in planformview, swept swing, tapered, variable wing, triangular, oblong,elliptical, square, among others. A wing's cross section geometry maycomprise an airfoil. An “airfoil” as used in this disclosure is a shapespecifically designed such that a fluid flowing above and below it exertdiffering levels of pressure against the top and bottom surface. Inembodiments, the bottom surface of an aircraft can be configured togenerate a greater pressure than does the top, resulting in lift.Laterally extending element may comprise differing and/or similarcross-sectional geometries over its cord length or the length from wingtip to where wing meets the aircraft's body. One or more wings may besymmetrical about the aircraft's longitudinal plane, which comprises thelongitudinal or roll axis reaching down the center of the aircraftthrough the nose and empennage, and the plane's yaw axis. Laterallyextending element may comprise controls surfaces configured to becommanded by a pilot or pilots to change a wing's geometry and thereforeits interaction with a fluid medium, like air. Control surfaces maycomprise flaps, ailerons, tabs, spoilers, and slats, among others. Thecontrol surfaces may dispose on the wings in a plurality of locationsand arrangements and in embodiments may be disposed at the leading andtrailing edges of the wings, and may be configured to deflect up, down,forward, aft, or a combination thereof. An aircraft, including adual-mode aircraft may comprise a combination of control surfaces toperform maneuvers while flying or on ground. In some embodiments,winglets may be provided at terminal ends of the wings which can provideimproved aerodynamic efficiency and stability in certain flightsituations. In some embodiments, the wings may be foldable to provide acompact aircraft profile, for example, for storage, parking and/or incertain flight modes.

Still referring to FIG. 1 , electric aircraft 100 includes a pluralityof flight components 108. As used in this disclosure a “flightcomponent” is a component that promotes flight and guidance of anaircraft. Flight component 108 may include power sources, control linksto one or more elements, fuses, and/or mechanical couplings used todrive and/or control any other flight component. Flight component 108may include a motor that operates to move one or more flight controlcomponents, to drive one or more propulsors, or the like. A motor may bedriven by direct current (DC) electric power and may include, withoutlimitation, brushless DC electric motors, switched reluctance motors,induction motors, or any combination thereof. A motor may also includeelectronic speed controllers or other components for regulating motorspeed, rotation direction, and/or dynamic braking. Flight component 108may include an energy source. An energy source may include, for example,a generator, a photovoltaic device, a fuel cell such as a hydrogen fuelcell, direct methanol fuel cell, and/or solid oxide fuel cell, anelectric energy storage device (e.g. a capacitor, an inductor, and/or abattery). An energy source may also include a battery cell, or aplurality of battery cells connected in series into a module and eachmodule connected in series or in parallel with other modules.Configuration of an energy source containing connected modules may bedesigned to meet an energy or power requirement and may be designed tofit within a designated footprint in an electric aircraft.

Still referring to FIG. 1 , in an embodiment, flight component 108 maybe mechanically coupled to an aircraft. As used herein, a person ofordinary skill in the art would understand “mechanically coupled” tomean that at least a portion of a device, component, or circuit isconnected to at least a portion of the aircraft via a mechanicalcoupling. Said mechanical coupling can include, for example, rigidcoupling, such as beam coupling, bellows coupling, bushed pin coupling,constant velocity, split-muff coupling, diaphragm coupling, disccoupling, donut coupling, elastic coupling, flexible coupling, fluidcoupling, gear coupling, grid coupling, hirth joints, hydrodynamiccoupling, jaw coupling, magnetic coupling, Oldham coupling, sleevecoupling, tapered shaft lock, twin spring coupling, rag joint coupling,universal joints, or any combination thereof. In an embodiment,mechanical coupling may be used to connect the ends of adjacent partsand/or objects of an electric aircraft. Further, in an embodiment,mechanical coupling may be used to join two pieces of rotating electricaircraft components.

Still referring to FIG. 1 , in an embodiment, plurality of flightcomponents 108 of aircraft 100 includes at least a lift component 112and at least a pusher component 116 which are described in furtherdetail later herein with reference to at least FIG. 2 . In anembodiment, aircraft 100 includes a pilot control 120. As used in thisdisclosure, a “pilot control” is a mechanism or means which allows apilot to monitor and control operation of aircraft such as its flightcomponents (for example, and without limitation, pusher component, liftcomponent and other components such as propulsion components). Forexample, and without limitation, pilot control 120 may include acollective, inceptor, foot bake, steering and/or control wheel, controlstick, pedals, throttle levers, and the like. Pilot control 120 may beconfigured to translate a pilot's desired torque for each flightcomponent of the plurality of flight components, such as and withoutlimitation, pusher component 116 and lift component 112. Pilot control120 may be configured to control, via inputs and/or signals such as froma pilot, the pitch, roll, and yaw of the aircraft. Pilot control may beavailable onboard aircraft or remotely located from it, as needed ordesired.

With continued reference to FIG. 1 , in some embodiments, electricaircraft 100 includes, or may be coupled to or communicatively connectedto, flight controller 124 which is described further with reference toFIG. 2 and FIG. 3 . As used in this disclosure a “flight controller” isa computing device of a plurality of computing devices dedicated to datastorage, security, distribution of traffic for load balancing, andflight instruction. In embodiments, flight controller may be installedin an aircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith. Flight controller 124, in an embodiment, is located withinfuselage 104 of aircraft. In accordance with some embodiments, flightcontroller is configured to operate a vertical lift flight (upwards ordownwards, that is, takeoff or landing), a fixed wing flight (forward orbackwards), a transition between a vertical lift flight and a fixed wingflight, and a combination of a vertical lift flight and a fixed wingflight.

Still referring to FIG. 1 , in an embodiment, and without limitation,flight controller 124 may be configured to operate a fixed-wing flightcapability. A “fixed-wing flight capability” can be a method of flightwherein the plurality of laterally extending elements generate lift. Forexample, and without limitation, fixed-wing flight capability maygenerate lift as a function of an airspeed of aircraft 100 and one ormore airfoil shapes of the laterally extending elements. As a furthernon-limiting example, flight controller 124 may operate the fixed-wingflight capability as a function of reducing applied torque on lift(propulsor) component 112. In an embodiment, and without limitation, anamount of lift generation may be related to an amount of forward thrustgenerated to increase airspeed velocity, wherein the amount of liftgeneration may be directly proportional to the amount of forward thrustproduced. Additionally or alternatively, flight controller may includean inertia compensator. As used in this disclosure an “inertiacompensator” is one or more computing devices, electrical components,logic circuits, processors, and the like there of that are configured tocompensate for inertia in one or more lift (propulsor) componentspresent in aircraft 100. Inertia compensator may alternatively oradditionally include any computing device used as an inertia compensatoras described in U.S. Nonprovisional application Ser. No. 17/106,557,filed on Nov. 30, 2020, and entitled “SYSTEM AND METHOD FOR FLIGHTCONTROL IN ELECTRIC AIRCRAFT,” the entirety of which is incorporatedherein by reference.

In an embodiment, and still referring to FIG. 1 , flight controller 124may be configured to perform a reverse thrust command. As used in thisdisclosure a “reverse thrust command” is a command to perform a thrustthat forces a medium towards the relative air opposing aircraft 100.Reverse thrust command may alternatively or additionally include anyreverse thrust command as described in U.S. Nonprovisional applicationSer. No. 17/319,155, filed on May 13, 2021, and entitled “AIRCRAFTHAVING REVERSE THRUST CAPABILITIES,” the entirety of which isincorporated herein by reference. In another embodiment, flightcontroller may be configured to perform a regenerative drag operation.As used in this disclosure a “regenerative drag operation” is anoperating condition of an aircraft, wherein the aircraft has a negativethrust and/or is reducing in airspeed velocity. For example, and withoutlimitation, regenerative drag operation may include a positive propellerspeed and a negative propeller thrust. Regenerative drag operation mayalternatively or additionally include any regenerative drag operation asdescribed in U.S. Nonprovisional application Ser. No. 17/319,155.

In an embodiment, and still referring to FIG. 1 , flight controller 124may be configured to perform a corrective action as a function of afailure event. As used in this disclosure a “corrective action” is anaction conducted by the plurality of flight components to correct and/oralter a movement of an aircraft. For example, and without limitation, acorrective action may include an action to reduce a yaw torque generatedby a failure event. Additionally or alternatively, corrective action mayinclude any corrective action as described in U.S. Nonprovisionalapplication Ser. No. 17/222,539, filed on Apr. 5, 2021, and entitled“AIRCRAFT FOR SELF-NEUTRALIZING FLIGHT,” the entirety of which isincorporated herein by reference. As used in this disclosure a “failureevent” is a failure of a lift component of the plurality of liftcomponents. For example, and without limitation, a failure event maydenote a rotation degradation of a rotor, a reduced torque of a rotor,and the like thereof. Additionally or alternatively, failure event mayinclude any failure event as described in U.S. Nonprovisionalapplication Ser. No. 17/113,647, filed on Dec. 7, 2020, and entitled“IN-FLIGHT STABILIZATION OF AN AIRCAFT,” the entirety of which isincorporated herein by reference.

Referring now to FIG. 2 , an exemplary embodiment of a system 200 forestimating flight range (or range of flight) of an electric aircraft,such as in some embodiments aircraft 100 of FIG. 1 , is illustrated. Inan embodiment, electric aircraft may include an electric verticaltakeoff and landing (eVTOL) aircraft.

Still referring to FIG. 2 , in some embodiments, system 200 generallyincludes at least a sensor 204 and a flight controller 124. At least asensor 204 is communicatively connected to at least a flight component108. At least a sensor 204 is configured to detect a performance datum208 of at least a flight component 108. Flight controller 124 iscommunicatively connected to at least a sensor 204. Flight controller124 is configured to receive performance datum 208 from at least asensor 204, determine an energy performance datum 212 from, or as afunction of, performance datum 208, determine a flight performance datum216 from, or as a function of, performance datum 208, generate aprojected flight range datum 220 as a function of energy performancedatum 212 and flight performance datum 216, and display projected flightrange datum 220. In an embodiment, system 200 may include a displaydevice 224, which may be onboard aircraft and/or remote from it, todisplay projected flight range datum 220. Display device 224 may, in anembodiment, be a part of flight controller 124.

Still referring to FIG. 2 , as used in this disclosure, “communicativelyconnected” is an attribute of a connection, attachment or linkage, wiredor wireless, direct or indirect, between two or more components,circuits, devices, systems, and the like, which allows for receptionand/or transmittance of data and/or signal(s) therebetween. Data and/orsignals therebetween may include, without limitation, electrical,electromagnetic, visual, audio, radio waves, combinations thereof, andthe like, among others. A communicative connection may be achieved, forexample and without limitation, through wired or wireless electronic,digital or analog, communication, either directly or by way of one ormore intervening devices or components. Further, communicativeconnection may include electrically coupling or connecting at least anoutput of one device, component, or circuit to at least an input ofanother device, component, or circuit. For example, and withoutlimitation, via a bus or other facility for intercommunication betweenelements of a computing device. Communicative connecting may alsoinclude indirect connections via, for example and without limitation,wireless connection, radio communication, low power wide area network,optical communication, magnetic, capacitive, or optical coupling, andthe like. In some instances, the terminology “communicatively coupled”may be used in place of communicatively connected in this disclosure.

Still referring to FIG. 2 , as used in this disclosure, a “sensor” is adevice that is configured to detect a phenomenon and transmitinformation related to the detection of the phenomenon. For example, insome cases a sensor may transduce a detected phenomenon, such as withoutlimitation, voltage, current, speed, direction, force, torque,temperature, pressure, and the like, into a sensed signal. At least asensor 204 may include one or more sensors which may be the same,similar or different. At least a sensor 204 may include a plurality ofsensors which may be the same, similar or different. At least a sensor204 may include one or more sensor suites with sensors in each sensorsuite being the same, similar or different. At least a sensor 204 mayinclude, for example and without limitation, a current sensor, a voltagesensor, a resistance sensor, a Wheatstone bridge, a gyroscope, anaccelerometer, a torque sensor, a magnetometer, an inertial measurementunit (IMU), a pressure sensor, a force sensor, a thermal sensor, aproximity sensor, a displacement sensor, a vibration sensor, a lightsensor, an optical sensor, a pitot tube, a speed sensor, and the like,among others. Sensors in accordance with embodiments disclosed hereinmay be configured detect a plurality of data, such as and withoutlimitation, data relating to battery life cycle, battery consumptionrate, flight path obstacles, weather, wind velocity, aircraft velocity,wind turbulence, battery temperature, and the like, among others.

Still referring to FIG. 2 , in an embodiment, at least a sensor 204 maybe mechanically connected or coupled to at least a flight component 108to detect a performance datum 208 of the at least a flight component 108and to transmit it to flight controller 124. As used in this disclosure,a “performance datum” is information on performance and/or operation ofa flight component of an aircraft. Performance datum 208 may include anyelement of data identifying and/or describing the parameters that mayaffect the flight and/or flight range of an electric aircraft. Forexample, and without limitation, performance datum 208 may include datadescribing battery performance and flight plan of electric aircraft. Inan embodiment, performance datum 208 may include information on energycapacity (or remaining energy capacity) of an energy source, such as andwithout limitation, one or more batteries or battery packs of electricaircraft.

With continued reference to FIG. 2 , at least a flight component 108 mayinclude a propulsor, a propeller, a motor, rotor, a rotating element,electrical energy source, battery, and the like, among others. Eachflight component may be configured to generate lift and flight ofelectric aircraft. In some embodiments, at least a flight component 108may include one or more lift components 112, one or more pushercomponents 116, one or more battery packs 228 including one or morebatteries 232, and one or more electric motors 236. In an embodiment, atleast a flight component 108 may include a battery 232. Alternatively oradditionally, in an embodiment, at least a flight component 108 mayinclude a propulsor. As used in this disclosure a “propulsor component”or “propulsor” is a component and/or device used to propel a craft byexerting force on a fluid medium, which may include a gaseous mediumsuch as air or a liquid medium such as water. In an embodiment, when apropulsor twists and pulls air behind it, it may, at the same time, pushan aircraft forward with an amount of force and/or thrust. More airpulled behind an aircraft results in greater thrust with which theaircraft is pushed forward. Propulsor component may include any deviceor component that consumes electrical power on demand to propel anelectric aircraft in a direction or other vehicle while on ground orin-flight.

Still referring to FIG. 2 , in some embodiments, at least a sensor 204is communicatively connected to battery pack(s) 228 and/or battery(ies)232 to receive performance datum 208. In some embodiments, battery packor system (or energy or power source) 228 is provided onboard electricaircraft (such as electric aircraft 100 of FIG. 1 ) to power flightcomponent(s) 108 via, for example, electrical energy. Battery pack 228may include one or more (or a plurality) of batteries 232, as needed ordesired.

Continuing to refer to FIG. 2 , in some embodiments, an electrical flowemanates or originates from an energy source onboard aircraft (e.g.electric aircraft 100 of FIG. 1 ) to power aircraft components such asflight components 108. In an embodiment, energy source includes batterypack (or system) 228. Battery pack 228 may include one or more batteries232 or battery cells. In some embodiments, more than one energy sourceor battery pack 228 may be provided onboard aircraft and may be situatedat different locations on the aircraft (e.g. electric aircraft 100 ofFIG. 1 ). Electrical flow may flow to, within, and from any ofaircraft's components, as needed or desired. In an embodiment,electrical flow includes electrical flow to, within and from one or moreof aircraft's flight components 204 such as, and without limitation,electric motor 236, lift component 112 and pusher component 116.Electric flow may travel along any suitable wire, cable, line, circuit,and the like, among others, on aircraft. As used in this disclosure, an“electrical flow” is a flow of charged particles (e.g. electrons) or anelectric current flowing within a material or structure which is capableof conducting it. Current may be measured in amperes or the like. Asused in this disclosure, a “battery pack” is a set of any number ofidentical (or non-identical) batteries or individual battery cells.These may be configured in a series, parallel or a mixture of bothconfiguration to deliver a desired electrical flow, current, voltage,capacity, or power density, as needed or desired. A battery may include,without limitation, one or more cells, in which chemical energy isconverted into electricity (or electrical energy) and used as a sourceof energy or power.

With continued reference to FIG. 2 , as used in this disclosure, an“energy source” is a source (or supplier) of energy (or power) to apower one or more components. For example, and without limitation,energy source may provide energy to a power source (e.g. electric motor236) that in turn that drives and/or controls any other aircraftcomponent such as other flight components. An energy source may include,for example, an electrical energy source a generator, a photovoltaicdevice, a fuel cell such as a hydrogen fuel cell, direct methanol fuelcell, and/or solid oxide fuel cell, an electric energy storage device(e.g., a capacitor, an inductor, and/or a battery). An electrical energysource may also include a battery cell, a battery pack, or a pluralityof battery cells connected in series into a module and each moduleconnected in series or in parallel with other modules. Configuration ofan energy source containing connected modules may be designed to meet anenergy or power requirement and may be designed to fit within adesignated footprint in an electric aircraft (e.g. electric aircraft 100of FIG. 1 ).

In an embodiment, and still referring to FIG. 2 , an energy source maybe used to provide a steady supply of electrical flow or power to a loadover the course of a flight by a vehicle or other electric aircraft. Forexample, an energy source may be capable of providing sufficient powerfor “cruising” and other relatively low-energy phases of flight. Anenergy source may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high state of charge (SOC), as may be the case forinstance during takeoff. In an embodiment, an energy source may becapable of providing sufficient electrical power for auxiliary loadsincluding without limitation, lighting, navigation, communications,de-icing, steering or other systems requiring power or energy. Further,an energy source may be capable of providing sufficient power forcontrolled descent and landing protocols, including, without limitation,hovering descent or runway landing. As used herein an energy source mayhave high power density where electrical power an energy source canusefully produce per unit of volume and/or mass is relatively high.“Electrical power,” as used in this disclosure, is defined as a rate ofelectrical energy per unit time. An energy source may include a devicefor which power that may be produced per unit of volume and/or mass hasbeen optimized, at the expense of the maximal total specific energydensity or power capacity, during design. Non-limiting examples of itemsthat may be used as at least an energy source may include batteries usedfor starting applications including Lithium ion (Li-ion) batteries whichmay include NCA, NMC, Lithium iron phosphate (LiFePO4) and LithiumManganese Oxide (LMO) batteries, which may be mixed with another cathodechemistry to provide more specific power if the application requires Limetal batteries, which have a lithium metal anode that provides highpower on demand, Li ion batteries that have a silicon or titanite anode,energy source may be used, in an embodiment, to provide electrical powerto an electric aircraft or drone, such as an electric aircraft vehicle,during moments requiring high rates of power output, including withoutlimitation takeoff, landing, thermal de-icing and situations requiringgreater power output for reasons of stability, such as high turbulencesituations, as described in further detail below. A battery may include,without limitation a battery using nickel based chemistries such asnickel cadmium or nickel metal hydride, a battery using lithium ionbattery chemistries such as a nickel cobalt aluminum (NCA), nickelmanganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobaltoxide (LCO), and/or lithium manganese oxide (LMO), a battery usinglithium polymer technology, lead-based batteries such as withoutlimitation lead acid batteries, metal-air batteries, or any othersuitable battery. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 2 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Amodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducean overall power output as a voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. Overall energy and poweroutputs of at least an energy source may be based on individual batterycell performance or an extrapolation based on measurement of at least anelectrical parameter. In an embodiment where an energy source includes aplurality of battery cells, overall power output capacity may bedependent on electrical parameters of each individual cell. If one cellexperiences high self-discharge during demand, power drawn from at leastan energy source may be decreased to avoid damage to the weakest cell.An energy source may further include, without limitation, wiring,conduit, housing, cooling system and battery management system. Personsskilled in the art will be aware, after reviewing the entirety of thisdisclosure, of many different components of an energy source.

Continuing to refer to FIG. 2 , energy sources, battery packs,batteries, sensors, sensor suites and/or associated methods which mayefficaciously be utilized in accordance with some embodiments aredisclosed in U.S. Nonprovisional application Ser. No. 17/111,002, filedon Dec. 3, 2020, entitled “SYSTEMS AND METHODS FOR A BATTERY MANAGEMENTSYSTEM INTEGRATED IN A BATTERY PACK CONFIGURED FOR USE IN ELECTRICAIRCRAFT,”, U.S. Nonprovisional application Ser. No. 17/108,798, filedon Dec. 1, 2020, and entitled “SYSTEMS AND METHODS FOR A BATTERYMANAGEMENT SYSTEM INTEGRATED IN A BATTERY PACK CONFIGURED FOR USE INELECTRIC AIRCRAFT,”, and U.S. Nonprovisional application Ser. No.17/320,329, filed on May 14, 2021, and entitled “SYSTEMS AND METHODS FORMONITORING HEALTH OF AN ELECTRIC VERTICAL TAKE-OFF AND LANDINGVEHICLE,”, the entirety of each one of which is incorporated herein byreference.

With continued reference to FIG. 2 , other energy sources, batterypacks, batteries, sensors, sensor suites and/or associated methods whichmay efficaciously be utilized in accordance with some embodiments aredisclosed in U.S. Nonprovisional application Ser. No. 16/590,496, filedon Oct. 2, 2019, and entitled “SYSTEMS AND METHODS FOR RESTRICTING POWERTO A LOAD TO PREVENT ENGAGING CIRCUIT PROTECTION DEVICE FOR ANAIRCRAFT,”, U.S. Nonprovisional application Ser. No. 17/348,137, filedon Jun. 15, 2021, and entitled “SYSTEMS AND METHODS FOR RESTRICTINGPOWER TO A LOAD TO PREVENT ENGAGING CIRCUIT PROTECTION DEVICE FOR ANAIRCRAFT,”, U.S. Nonprovisional application Ser. No. 17/008,721, filedon Sep. 1, 2020, and entitled “SYSTEM AND METHOD FOR SECURING BATTERY INAIRCRAFT,”, U.S. Nonprovisional application Ser. No. 16/948,157, filedon Sep. 4, 2020, and entitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITYBATTERY MODULE,”, U.S. Nonprovisional application Ser. No. 16/948,140,filed on Sep. 4, 2020, and entitled “SYSTEM AND METHOD FOR HIGH ENERGYDENSITY BATTERY MODULE,”, and U.S. Nonprovisional application Ser. No.16/948,141, filed on Sep. 4, 2020, and entitled “COOLING ASSEMBLY FORUSE IN A BATTERY MODULE ASSEMBLY,”, the entirety of each one of which isincorporated herein by reference.

Still referring to FIG. 2 , in some embodiments, at least a sensor 204is communicatively connected to lift component(s) 112 to receiveperformance datum 208. Lift component 112 may include a propulsor, apropeller, a blade, a motor, a rotor, a rotating element, an aileron, arudder, arrangements thereof, combinations thereof, and the like. Eachlift component 112, when a plurality is present, of plurality of flightcomponents 108 is configured to produce, in an embodiment, substantiallyupward and/or vertical thrust such that aircraft moves upward.

With continued reference to FIG. 2 , as used in this disclosure a “liftcomponent” is a component and/or device used to propel a craft upward byexerting downward force on a fluid medium, which may include a gaseousmedium such as air or a liquid medium such as water. Lift component 112may include any device or component that consumes electrical power ondemand to propel an electric aircraft in a direction or other vehiclewhile on ground or in-flight. For example, and without limitation, liftcomponent 112 may include a rotor, propeller, paddle wheel and the likethereof, wherein a rotor is a component that produces torque along thelongitudinal axis, and a propeller produces torque along the verticalaxis. In an embodiment, lift component 112 includes a plurality ofblades. As used in this disclosure a “blade” is a propeller thatconverts rotary motion from an engine or other power source into aswirling slipstream. In an embodiment, blade may convert rotary motionto push the propeller forwards or backwards. In an embodiment liftcomponent 112 may include a rotating power-driven hub, to which areattached several radial airfoil-section blades such that the wholeassembly rotates about a longitudinal axis. Blades may be configured atan angle of attack. In an embodiment, and without limitation, angle ofattack may include a fixed angle of attack. As used in this disclosure a“fixed angle of attack” is fixed angle between a chord line of a bladeand relative wind. As used in this disclosure a “fixed angle” is anangle that is secured and/or unmovable from the attachment point. In anembodiment, and without limitation, angle of attack may include avariable angle of attack. As used in this disclosure a “variable angleof attack” is a variable and/or moveable angle between a chord line of ablade and relative wind. As used in this disclosure a “variable angle”is an angle that is moveable from an attachment point. In an embodiment,angle of attack be configured to produce a fixed pitch angle. As used inthis disclosure a “fixed pitch angle” is a fixed angle between a cordline of a blade and the rotational velocity direction. In an embodimentfixed angle of attack may be manually variable to a few set positions toadjust one or more lifts of the aircraft prior to flight. In anembodiment, blades for an aircraft are designed to be fixed to their hubat an angle similar to the thread on a screw makes an angle to theshaft; this angle may be referred to as a pitch or pitch angle whichwill determine a speed of forward movement as the blade rotates.

In an embodiment, and still referring to FIG. 2 , lift component 112 maybe configured to produce a lift. As used in this disclosure a “lift” isa perpendicular force to the oncoming flow direction of fluidsurrounding the surface. For example, and without limitation relativeair speed may be horizontal to the aircraft, wherein lift force may be aforce exerted in a vertical direction, directing the aircraft upwards.In an embodiment, and without limitation, lift component 112 may producelift as a function of applying a torque to lift component. As used inthis disclosure a “torque” is a measure of force that causes an objectto rotate about an axis in a direction. For example, and withoutlimitation, torque may rotate an aileron and/or rudder to generate aforce that may adjust and/or affect altitude, airspeed velocity,groundspeed velocity, direction during flight, and/or thrust. Forexample, one or more flight components 108 such as a power source(s) mayapply a torque on lift component 112 to produce lift.

In an embodiment and still referring to FIG. 2 , a plurality of liftcomponents 112 of the plurality of flight components 108 (FIG. 1 ) maybe arranged in a quad copter orientation. As used in this disclosure a“quad copter orientation” is at least a lift component oriented in ageometric shape and/or pattern, wherein each of the lift components islocated along a vertex of the geometric shape. For example, and withoutlimitation, a square quad copter orientation may have four liftpropulsor components oriented in the geometric shape of a square,wherein each of the four lift propulsor components are located along thefour vertices of the square shape. As a further non-limiting example, ahexagonal quad copter orientation may have six lift components orientedin the geometric shape of a hexagon, wherein each of the six liftcomponents are located along the six vertices of the hexagon shape. Inan embodiment, and without limitation, quad copter orientation mayinclude a first set of lift components and a second set of liftcomponents, wherein the first set of lift components and the second setof lift components may include two lift components each, wherein thefirst set of lift components and a second set of lift components aredistinct from one another. For example, and without limitation, thefirst set of lift components may include two lift components that rotatein a clockwise direction, wherein the second set of lift propulsorcomponents may include two lift components that rotate in acounterclockwise direction. In an embodiment, and without limitation,the first set of lift components may be oriented along a line oriented45° from the longitudinal axis of aircraft 100 (FIG. 1 ). In anotherembodiment, and without limitation, the second set of lift componentsmay be oriented along a line oriented 135° from the longitudinal axis,wherein the first set of lift components line and the second set of liftcomponents are perpendicular to each other.

Still referring to FIG. 2 , pusher component 116 and lift component 112(of flight component(s) 108) may include any such components and relateddevices as disclosed in U.S. Nonprovisional application Ser. No.16/427,298, filed on May 30, 2019, entitled “SELECTIVELY DEPLOYABLEHEATED PROPULSOR SYSTEM,”, U.S. Nonprovisional application Ser. No.16/703,225, filed on Dec. 4, 2019, entitled “AN INTEGRATED ELECTRICPROPULSION ASSEMBLY,”, U.S. Nonprovisional application Ser. No.16/910,255, filed on Jun. 24, 2020, entitled “AN INTEGRATED ELECTRICPROPULSION ASSEMBLY,”, U.S. Nonprovisional application Ser. No.17/319,155, filed on May 13, 2021, entitled “AIRCRAFT HAVING REVERSETHRUST CAPABILITIES,”, U.S. Nonprovisional application Ser. No.16/929,206, filed on Jul. 15, 2020, entitled “A HOVER AND THRUST CONTROLASSEMBLY FOR DUAL-MODE AIRCRAFT,”, U.S. Nonprovisional application Ser.No. 17/001,845, filed on Aug. 25, 2020, entitled “A HOVER AND THRUSTCONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT,”, U.S. Nonprovisionalapplication Ser. No. 17/186,079, filed on Feb. 26, 2021, entitled“METHODS AND SYSTEM FOR ESTIMATING PERCENTAGE TORQUE PRODUCED BY APROPULSOR CONFIGURED FOR USE IN AN ELECTRIC AIRCRAFT,”, and U.S.Nonprovisional application Ser. No. 17/321,662, filed on May 17, 2021,entitled “AIRCRAFT FOR FIXED PITCH LIFT,”, the entirety of each one ofwhich is incorporated herein by reference.

Still referring to FIG. 2 , pusher component 116 may include apropulsor, a propeller, a blade, a motor, a rotor, a rotating element,an aileron, a rudder, arrangements thereof, combinations thereof, andthe like. Each pusher component 116, when a plurality is present, of theplurality of flight components 108 is configured to produce, in anembodiment, substantially forward and/or horizontal thrust such that theaircraft moves forward.

Still referring to FIG. 2 , in some embodiments, at least a sensor 204is communicatively connected to pusher component(s) 116 to receiveperformance datum 208. As used in this disclosure a “pusher component”is a component that pushes and/or thrusts an aircraft through a medium.As a non-limiting example, pusher component 116 may include a pusherpropeller, a paddle wheel, a pusher motor, a pusher propulsor, and thelike. Additionally, or alternatively, pusher flight component mayinclude a plurality of pusher flight components. Pusher component 116 isconfigured to produce a forward thrust. As a non-limiting example,forward thrust may include a force to force aircraft to in a horizontaldirection along the longitudinal axis. As a further non-limitingexample, pusher component 116 may twist and/or rotate to pull air behindit and, at the same time, push aircraft 100 (FIG. 1 ) forward with anequal amount of force. In an embodiment, and without limitation, themore air forced behind aircraft, the greater the thrust force with whichthe aircraft is pushed horizontally will be. In another embodiment, andwithout limitation, forward thrust may force aircraft 100 through themedium of relative air. Additionally or alternatively, plurality offlight components 108 may include one or more puller components. As usedin this disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

Still referring to FIG. 2 , in some embodiments, at least a sensor 204is communicatively connected to power source or electric motor(s) 236 toreceive performance datum 208. As used in this disclosure a “powersource” is a source that drives and/or controls any flight componentand/or other aircraft component. For example, and without limitationpower source may include motor 236 that operates to move one or morelift components 112 and/or one or more pusher components 116, to driveone or more blades, or the like thereof. Motor(s) 236 may be driven bydirect current (DC) electric power and may include, without limitation,brushless DC electric motors, switched reluctance motors, inductionmotors, or any combination thereof. Motor(s) 236 may also includeelectronic speed controllers or other components for regulating motorspeed, rotation direction, and/or dynamic braking. A “motor” as used inthis disclosure is any machine that converts non-mechanical energy intomechanical energy. An “electric motor” as used in this disclosure is anymachine that converts electrical energy into mechanical energy.

With continued reference to FIG. 2 , embodiments as disclosed hereinencompass sensors which may be used to detect performance of aircraftcomponents, such as flight components, as well as sensors which may beused to detect other phenomena associated with aircraft (e.g. electricaircraft 100 of FIG. 1 ). These other sensors may include, withoutlimitation, a weather sensor, an external environment sensor, a windsensor, an air speed sensor, and the like, among others, and may beprovided on aircraft or, in some cases, remote from it.

Still referring to FIG. 2 , in an embodiment, at least a sensor 204 mayinclude a torque sensor communicatively connected to a propulsor, liftcomponent or pusher component to detect its performance. For example,and without limitation, torque sensor may include a load cell, such asan in-line torque load cell, a device to measure torque using existingcurrent measurement and/or voltage estimates at an inverter or motorlevel, such as with a current sensor, voltage sensor, or any othercomponent suitable for sensing torque. In an embodiment, and withoutlimitation, torque may be a measure of a force which rotates propulsor.At least a sensor 204, in an embodiment, may include a speed sensor, forexample, and without limitation, as speed sensor to detect rotationalspeed of propulsor in revolutions per minute (RPM). Some torque andspeed sensors which may efficaciously be utilized in accordance withcertain embodiments are disclosed in U.S. Nonprovisional applicationSer. No. 17/361,463, filed on Jun. 29, 2021, entitled “SYSTEM AND METHODFOR AIRSPEED ESTIMATION UTILIZING PROPULSOR DATA IN ELECTRIC VERTICALTAKEOFF AND LANDING AIRCRAFT,” the entirety of which is incorporatedherein by reference.

Still referring to FIG. 2 , in an embodiment, flight controller 124 isconfigured to receive an external environment datum and generateprojected flight range datum 220 as a function of the externalenvironment datum. As used in this disclosure, an “external environmentdatum” is any information on a condition external to an aircraft whichmay affect aircraft flight. For example, and without limitation, weatherand wind conditions and any turbulence around aircraft. These conditionsmay be detected by one or more sensors on or onboard aircraft.Additionally, data on such conditions, and the like, along aircraft'sprojected trajectory may be provided by remote sensors. Certainenvironmental and weather sensors which may efficaciously be utilized inaccordance with some embodiments are disclosed in U.S. Nonprovisionalapplication Ser. No. 17/374,055, filed on Jul. 13, 2021, entitled“SYSTEM AND METHOD FOR AUTOMATED AIR TRAFFIC CONTROL,”, the entirety ofwhich is incorporated herein by reference.

Still referring to FIG. 2 , in an embodiment, system 200 may include aweather sensor 240 configured to detect a weather datum external toelectric aircraft. Flight performance datum 216 may be determined fromweather datum. For example, flight controller 124 may generate flightperformance datum 216 based on weather datum. Weather sensor 240 may beon or onboard aircraft or remote from it.

Still referring to FIG. 2 , as used in this disclosure, a “weathersensor” may include any sensor configured to detect, sense and/ormeasure a weather datum in an environment exterior to aircraft. Forexample, and without limitation, weather sensor may be configured todetect the temperature, humidity, pressure, air or wind speed and/ordirection, moisture content, fog level, and the like, and may include,without limitation, an inertial measurement unit (IMU), gyroscope,temperature sensor, proximity sensor, pressure sensor, light sensor, airspeed sensor, and the like. Weather sensor may include, withoutlimitation, an all-in-one weather sensor which is configured to measuremultiple parameters, such as, and without limitation, wind speed anddirection, precipitation, barometric pressure, temperature, relativehumidity, and the like among other parameters. In some embodiments, theat least a sensor includes a sensor external to the aircraft and locatedat or connected to an external source. For example, and withoutlimitation, weather sensor may be at a weather service, airport,airplane, local tower, connected to the internet, and the like. As usedin this disclosure, a “weather datum” is a metric describing a state ofweather, such as, without limitation, that of an environment exterior toan aircraft. For example, and without limitation, weather datum mayinclude a temperature, humidity, pressure, air or wind speed and/ordirection, moisture content, fog level, and the like. Weather datum mayalso include historical data on the weather and future projectionsrelating to the weather.

Still referring to FIG. 2 , in an embodiment, system 200 may include anair speed sensor 244 configured to detect an air speed datum external toelectric aircraft. As used in this disclosure, an “air speed datum” isinformation on the speed and/or direction of air flow. For example, andwithout limitation, the speed and/or direction of ambient air flowing orwind moving external to aircraft. Flight performance datum 216 may bedetermined from air speed datum. For example, flight controller 124 maygenerate flight performance datum 216 based on air speed datum. Airspeed sensor 240 may be on or onboard aircraft or remote from it. An“air speed sensor” as used in this disclosure is any sensor configuredto detect, sense and/or measure an air speed datum in an environmentexterior to aircraft. For example, and without limitation, an air speeddatum may include wind speed and/or direction. Air speed or wind sensorsmay include, without limitation, anemometers, pitot tubes, hot-wiresensors, laser doppler sensors, ultrasonic sensors, pressure sensors,and the like among others.

With continued reference to FIG. 2 , flight controller 124 may includeone or more computing devices. Computing device may include anycomputing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. Computing devicemay include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device may interface or communicate with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting computing device to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device may include but is not limited to, for example,a computing device or cluster of computing devices in a first locationand a second computing device or cluster of computing devices in asecond location. Computing device may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Computing device may distribute one ormore computing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 100and/or computing device.

With continued reference to FIG. 2 , computing device may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing devicemay be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 2 , in some embodiments, flight controller 124may be configured to communicate (e.g. receive and/or transmit dataand/or signals) with one or more sensors, including at least a sensor204, weather sensor 240 and air speed sensor 244, and/or flightcomponent(s) 108, including lift component(s) 112, pusher component(s)116, battery pack(s) 228 including battery(ies) 232, and electricmotor(s) 236. Flight controller 124, in an embodiment, may include anycomputing device and/or combination of computing devices programmed tooperate electric aircraft. In an embodiment, flight controller 124 maybe onboard electric aircraft. In another embodiment, flight controller124 may be remote from electric aircraft. As used in this disclosure,“remote” is a spatial separation between two or more elements, systems,components or devices. Stated differently, two elements may be remotefrom one another if they are physically spaced apart. In an embodiment,flight controller 124 may include a proportional-integral-derivative(PID) controller.

Still referring to FIG. 2 , in an embodiment, flight controller 124 isconfigured to receive performance datum 208 from at least a sensor 204,determine an energy performance datum 212 from, or as a function of,performance datum 208, determine a flight performance datum 216 from, oras a function of, performance datum 208, generate a projected flightrange datum 220 as a function of energy performance datum 212 and flightperformance datum 216, and display projected flight range datum 220. Asalso discussed above, in some embodiments, flight controller 124 mayalso be configured to receive other data, such as, and withoutlimitation, external environment data, weather data and/or air speed orwind data, from one or more of an external environment sensor, weathersensor 240 and air speed or wind sensor 244. This data may also beutilized, as needed or desired, in determining flight performance datum216 and subsequently in generating projected flight range datum 220.

With continued reference to FIG. 2 , in an embodiment, performance datum208 may include information on energy capacity of an energy source, forexample, and without limitation, a metric relating to remaining energyof battery pack 228 and/or battery 232. In an embodiment, energyperformance datum 212 may include information on state of charge (SOC)of a battery, for example, and without limitation, a metric relating toremaining charge of battery pack 228 and/or battery 232. In anembodiment, flight performance datum 216 may include information ontorque of a propulsor, for example, and without limitation, a metricrelating to torque and/or rotational speed (RPM) of a lift component 112and/or pusher component 116.

Still referring to FIG. 2 , as used in this disclosure, “energyperformance datum” is information on performance and/or operation of anenergy source of an aircraft. Energy source may include, withoutlimitation, a battery system, a battery module, a battery unit, abattery pack, a battery, an electrochemical cell or a battery cell,among others. For example, in some embodiments, energy source mayinclude one or more battery packs 228 and/or battery(ies) 232 or batterycell(s). Energy performance datum may include, without limitation,battery performance datum such as, without limitation, an element ofdata including battery state of charge (SOC), battery capacity, batteryoutput rate, battery life cycle, battery consumption rate, batterytemperature, electrical integrity, ground fault, short circuit, batteryambient conditions, and the like, among others. Energy performance datummay include, without limitation, any metric relating to current and/orprojected energy capacity of energy source, such as, without limitationan energy source including one or more batteries or batteries cells.Certain energy sources, battery packs, batteries and associated sensorsto measure performance which may efficaciously be utilized in accordancewith some embodiments are disclosed in U.S. Nonprovisional applicationSer. No. 17/405,840, filed on Aug. 18, 2021, entitled “CONNECTOR ANDMETHODS OF USE FOR CHARGING AN ELECTRIC VEHICLE,”, the entirety of whichis incorporated herein by reference.

Still referring to FIG. 2 , as used in this disclosure, a “flightperformance datum” is information on performance and/or operation of anaircraft component that affects flight of an aircraft. For example, andwithout limitation, flight performance datum may include an element ofdata identifying and/or describing the parameters for a flight plan.Aircraft components may include, without limitation, any flightcomponents 108 including, for example and without limitation, liftcomponent(s) 112, pusher component(s) 116, and electric motor(s) 236. Insome embodiments, aircraft components may include energy sourcecomponents. Flight performance datum may include, without limitation,any metric relating to propulsor torque, propulsor rotational speed(RPM), motor efficiency, altitude, attitude, pitch, yaw, roll, intendedflight plan, flight trajectory, predicted flight path, maximum flightpath, minimum flight path, and the like, among others.

With continued reference to FIG. 2 , “projected flight range datum” isinformation on an estimate of flight range of an aircraft. Thisinformation may be updated in real-time as the flight progresses, asneeded or desired. Projected flight range datum may include any elementof data describing a range estimation that electric aircraft may flywithin to operate safely to a desired location. In an embodiment,generating projected flight range datum may include, without limitation,identifying a flight plan wherein a battery temperature is within athermal threshold. For example, and without limitation, thermalthreshold may include upper and lower thermal limits that batterytemperature may deviate from to ensure it does not fall outside of thethreshold to avoid battery blowout. In an embodiment, generatingprojected flight range datum may include, without limitation,identifying a flight plan wherein battery performance falls within abattery usage threshold. Battery usage threshold may include upper andlower battery consumption rate limits that the battery consumption ratemay deviate from to ensure it does not fall outside the threshold togenerate a projected flight range datum for electric aircraft.

Still referring to FIG. 2 , in some embodiments, flight controller 124may be configured to display projected flight range datum 220 on displaydevice 224. Display device 224 may include a computing device or be partof a computing device. Computing device may include any of the computingdevices as disclosed herein. Display device 224 may be onboard aircraftand/or remote from it. Display device 224 may, in an embodiment, be apart of flight controller 124. Display device 224 may be configured todisplay projected flight range datum 220 in any suitable manner, forexample, and without limitation, by a visual, video, audio, and thelike, among others, display and/or notification. Display device 224 mayinclude, without limitation, a fleet management device, a flightsimulator, a graphical user interface (GUI), a multi-function display(MFD), a mobile device, a tablet, and the like, among others. Display ofthe projected flight range datum 220 may include, without limitation anumerical range of a distance unit including miles, kilometers, and thelike. Projected flight range datum 220 being displayed may include,without limitation, a flight path indicator including a line, dottedline, graphical line, and the like.

With continued reference to FIG. 2 , in an embodiment, flight controller124 may generate and/or calculate projected flight range datum 220 (orflight range estimate) as a function of aircraft speed and environment.Additionally, or alternatively, in an embodiment, generation of flightrange datum 220 may factor in that battery performance may dramaticallydrop off as battery empties or discharges. Some embodiments may include,for example and without limitation, measures to ensure that energysource or battery is operating in safe range and does not exceed anythermal limit which may entail additional corrective actions.

Still referring to FIG. 2 , in some embodiments, flight controller 124may be configured to generate projected flight range datum 220 based onan algorithm which uses data on a battery's current state of charge(SOC) and projected predictions moving along an SOC curve. Machinelearning algorithms and/or models may also be efficaciously utilized, asneeded or desired.

Still referring to FIG. 2 , simply stated, projected flight range datum220 is letting a user, for example, and without limitation, a pilot,know amount of remaining energy in aircraft's energy source (e.g.battery). That is, amount of power left until energy source or batteryempties. Projected flight range datum 220 may be displayed to user inany suitable manner. For example, and without limitation, displayed to apilot onboard aircraft, transmitted to a flight simulator from where apilot is controlling aircraft, transmitted to a ground station such asan airport, air traffic control facility, fleet management facility,recharging station, and the like, among others. Projected flight rangedatum 220 may be described in any suitable manner using any suitablemetric, for example, and without limitation, time of flight left (suchas in hours and/or minutes), distance of flight range remaining (such asin miles or kilometers), and the like, among others.

Now referring to FIG. 3 , an exemplary embodiment 300 of a flightcontroller 304 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 304 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 304may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 304 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a signal transformation component 308. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 308 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component308 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 308 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 308 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 308 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 308 may beconfigured to optimize an intermediate representation 312. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 308 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 308 may optimizeintermediate representation 312 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 308 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 308 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 304. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 308 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 320. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 320 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 320 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 320 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 320 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 320 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 312. Logiccomponent 320 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 304. Logiccomponent 320 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 320 may beconfigured to execute the instruction on intermediate representation 312and/or output language. For example, and without limitation, logiccomponent 320 may be configured to execute an addition operation onintermediate representation 312 and/or output language.

In an embodiment, and without limitation, logic component 320 may beconfigured to calculate a flight element 324. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 324 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 324 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 324 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 3 , flight controller 304 may include a chipsetcomponent 328. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 328 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 320 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 328 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 320 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 328 maymanage data flow between logic component 320, memory cache, and a flightcomponent 108. As used in this disclosure (and with particular referenceto FIG. 3 ) a “flight component” is a portion of an aircraft that can bemoved or adjusted to affect one or more flight elements. For example,flight component 108 may include a component used to affect theaircrafts' roll and pitch which may comprise one or more ailerons. As afurther example, flight component 108 may include a rudder to controlyaw of an aircraft. In an embodiment, chipset component 328 may beconfigured to communicate with a plurality of flight components as afunction of flight element 324. For example, and without limitation,chipset component 328 may transmit to an aircraft rotor to reduce torqueof a first lift propulsor and increase the forward thrust produced by apusher component to perform a flight maneuver.

In an embodiment, and still referring to FIG. 3 , flight controller 304may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 304 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 324. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 304 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 304 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 3 , flight controller 304may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 324 and a pilot signal336 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 336may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 336 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 336may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 336 may include an explicitsignal directing flight controller 304 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 336 may include an implicit signal, wherein flight controller 304detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 336 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 336 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 336 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 336 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal336 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 304 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 304.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 304 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller 304 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 304. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 304 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 304 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 3 , flight controller 304 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller304 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 304 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 304 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 108. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 304. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 312 and/or output language from logiccomponent 320, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller 304 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 304 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x; that may receive numerical values from inputs toa neural network containing the node and/or from other nodes. Node mayperform a weighted sum of inputs using weights w, that are multiplied byrespective inputs xi. Additionally or alternatively, a bias b may beadded to the weighted sum of the inputs such that an offset is added toeach unit in the neural network layer that is independent of the inputto the layer. The weighted sum may then be input into a function p,which may generate one or more outputs y. Weight w, applied to an inputx; may indicate whether the input is “excitatory,” indicating that ithas strong influence on the one or more outputs y, for instance by thecorresponding weight having a large numerical value, and/or a“inhibitory,” indicating it has a weak effect influence on the one moreinputs y, for instance by the corresponding weight having a smallnumerical value. The values of weights w, may be determined by traininga neural network using training data, which may be performed using anysuitable process as described above. In an embodiment, and withoutlimitation, a neural network may receive semantic units as inputs andoutput vectors representing such semantic units according to weights w,that are derived using machine-learning processes as described in thisdisclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 340. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 304 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 340may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 340 may include any component of any flightcontroller as described above. Sub-controller 340 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 340may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 340 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 3 , flight controller may include aco-controller 344. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 304 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 344 mayinclude one or more controllers and/or components that are similar toflight controller 304. As a further non-limiting example, co-controller344 may include any controller and/or component that joins flightcontroller 304 to distributer flight controller. As a furthernon-limiting example, co-controller 344 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 304 to distributed flight control system. Co-controller 344may include any component of any flight controller as described above.Co-controller 344 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller 304 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 304 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Now referring to FIG. 5 , an exemplary embodiment of a method 500 forestimating flight range of an electric aircraft is illustrated. Aircraftmay be any of the aircrafts as disclosed herein and described above withreference to at least FIG. 1 . In an embodiment, electric aircraft mayinclude an electric vertical takeoff and landing (eVTOL) aircraft.

Still referring to FIG. 5 , at step 505, a performance datum of at leasta flight component is detected by at least a sensor communicativelyconnected to the at least a flight component. Performance datum mayinclude any of the performance datums as disclosed herein and describedabove with reference to at least FIG. 2 . Flight component may includeany of the flight components as disclosed herein and described abovewith reference to at least FIG. 1 and FIG. 2 . At least a sensor mayinclude any of the sensors as disclosed herein and described above withreference to at least FIG. 2 . Detection may include any means ofdetection as described in the entirety of this disclosure.

Still referring to FIG. 5 , at step 510, performance datum from at leasta sensor is received by a flight controller communicatively connected tothe at least a sensor. Flight controller may include any of the flightcontrollers as disclosed herein and described above with reference to atleast FIG. 1 , FIG. 2 and FIG. 3 .

Still referring to FIG. 5 , at step 515, an energy performance datum isdetermined from the performance datum by flight controller. Energyperformance datum may include any of the energy performance datums asdisclosed herein and described above with reference to at least FIG. 2 .

Still referring to FIG. 5 , at step 520, a flight performance datum isdetermined from the performance datum by flight controller. Flightperformance datum may include any of the flight performance datums asdisclosed herein and described above with reference to at least FIG. 2 .

Continuing to refer to FIG. 5 , at step 525, a projected flight rangedatum is generated, by flight controller, as a function of energyperformance datum and flight performance datum. Projected flight rangedatum may include any of the projected flight range datums as disclosedherein and described above with reference to at least FIG. 2 .Generation may include any means of generation as described in theentirety of this disclosure.

With continued reference to FIG. 5 , at step 530, projected flight rangedatum is displayed by flight controller. Display of projected flightrange datum may include, without limitation, a visual display, an audiodisplay, a video display, and the like among others. Projected flightrange may be displayed to multiple parties, for example, on aircraft orremote from it. Display may be at a device other than flight controller,for example and without limitation, at a flight management facility, airtraffic control facility, fleet management facility, flight simulator,remote device or computing device, ground station, airport, helipad,recharging station, and the like, among others.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for estimating flight range of anelectric aircraft, the system comprising: at least a sensorcommunicatively connected to at least a flight component, wherein the atleast a sensor is configured to detect a performance datum of the atleast a flight component; and a flight controller communicativelyconnected to the at least a sensor, wherein the flight controller isconfigured to: receive the performance datum from the at least a sensor;determine an energy performance datum from the performance datum;determine a flight performance datum from the performance datum;generate a projected flight range datum as a function of the energyperformance datum and the flight performance datum; and display theprojected flight range datum.
 2. The system of claim 1, wherein the atleast a flight component comprises a battery.
 3. The system of claim 2,wherein the at least a flight component further comprises a propulsor.4. The system of claim 1, wherein the performance datum comprisesinformation on energy capacity of an energy source.
 5. The system ofclaim 1, wherein the energy performance datum comprises information onstate of charge of a battery.
 6. The system of claim 1, wherein theflight performance datum comprises information on torque of a propulsor.7. The system of claim 1, wherein the flight controller is furtherconfigured to: receive an external environment datum; and generate theprojected flight range datum as a function of the external environmentdatum.
 8. The system of claim 1, wherein the system further comprises aweather sensor configured to detect a weather datum external to theelectric aircraft, and wherein the flight performance datum is furtherdetermined from the weather datum.
 9. The system of claim 1, wherein thesystem further comprises an air speed sensor configured to detect an airspeed datum external to the electric aircraft, and wherein the flightperformance datum is further determined from the air speed datum. 10.The system of claim 1, wherein the electric aircraft comprises anelectric vertical takeoff and landing (eVTOL) aircraft.
 11. A method forestimating flight range of an electric aircraft, the method comprising:detecting, by at least a sensor communicatively connected to at least aflight component, a performance datum of the at least a flightcomponent; receiving, by a flight controller communicatively connectedto the at least a sensor, the performance datum from the at least asensor; determining, by the flight controller, an energy performancedatum from the performance datum; determining, by the flight controller,a flight performance datum from the performance datum; generating, bythe flight controller, a projected flight range datum as a function ofthe energy performance datum and the flight performance datum; anddisplaying, by the flight controller, the projected flight range datum.12. The method of claim 11, wherein the at least a flight componentcomprises a battery.
 13. The method of claim 12, wherein the at least aflight component further comprises a propulsor.
 14. The method of claim11, wherein the performance datum comprises information on energycapacity of an energy source.
 15. The method of claim 11, wherein theenergy performance datum comprises information on state of charge of abattery.
 16. The method of claim 11, wherein the flight performancedatum comprises information on torque of a propulsor.
 17. The method ofclaim 11, wherein the method further comprises: receiving, by the flightcontroller, an external environment datum; and generating, by the flightcontroller, the projected flight range datum as a function of theexternal environment datum.
 18. The method of claim 11, wherein themethod further comprises detecting, by a weather sensor, a weather datumexternal to the electric aircraft, and wherein the flight performancedatum is further determined from the weather datum.
 19. The method ofclaim 11, wherein the method further comprises detecting, by an airspeed sensor, an air speed datum external to the electric aircraft, andwherein the flight performance datum is further determined from the airspeed datum.
 20. The method of claim 11, wherein the electric aircraftcomprises an electric vertical takeoff and landing (eVTOL) aircraft.